Fault Diagnosis in Industrial Induction Machines Through Discrete Wavelet Transform

被引：240

作者：

Bouzida, Ahcene ^{[1
]}

Touhami, Omar ^{[1
]}

Ibtiouen, Rachid ^{[1
]}

Belouchrani, Adel ^{[1
]}

Fadel, Maurice

Rezzoug, A. ^{[2
]}

机构：

[1] Ecole Natl Polytech, Algiers 16200, Algeria

[2] Ecole Natl Super Electricite & Mecan, F-54516 Vandoeuvre Les Nancy, France

来源：

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS | 2011年 / 58卷 / 09期

关键词：

Broken rotor bars; data-dependent selection (DDS) and data-independent selection (DIS) of the decomposition level; fault diagnosis; induction machines (IMs); motor-current signature analysis (MCSA); wavelet transform; SIGNATURE ANALYSIS; ONLINE DIAGNOSIS; ALGORITHM; DESIGN;

D O I：

10.1109/TIE.2010.2095391

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper deals with fault diagnosis of induction machines based on the discrete wavelet transform. By using the wavelet decomposition, the information on the health of a system can be extracted from a signal over a wide range of frequencies. This analysis is performed in both time and frequency domains. The Daubechies wavelet is selected for the analysis of the stator current. Wavelet components appear to be useful for detecting different electrical faults. In this paper, we will study the problem of broken rotor bars, end-ring segment, and loss of stator phase during operation.

引用

收藏

页码：4385 / 4395

页数：11

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